Spatial Dependence and Data-Driven Networks of International Banks

46 Pages Posted: 5 Dec 2016

See all articles by Ben R. Craig

Ben R. Craig

Federal Reserve Bank of Cleveland; Deutsche Bundesbank

Martin Saldias

European Central Bank (ECB)

Multiple version iconThere are 3 versions of this paper

Date Written: December 2, 2016

Abstract

This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to select the significant bilateral correlations. The analysis of topological characteristics of the resulting correlation networks shows many common features that have been documented in the recent literature but were obtained with private information on banks’ exposures. Our analysis validates these market-based adjacency matrices as inputs for the spatio-temporal analysis of shocks in the banking system.

Keywords: Network analysis, spatial dependence, banking

JEL Classification: C21, C23, C45, G21

Suggested Citation

Craig, Ben R. and Saldias, Martin, Spatial Dependence and Data-Driven Networks of International Banks (December 2, 2016). FRB of Cleveland Working Paper No. 16-27, Available at SSRN: https://ssrn.com/abstract=2879618

Ben R. Craig (Contact Author)

Federal Reserve Bank of Cleveland ( email )

PO Box 6387
Cleveland, OH 44101
United States
216-579-2061 (Phone)
216-579-3050 (Fax)

Deutsche Bundesbank

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

Martin Saldias

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

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